High-Precision Chip Detection Using YOLO-Based Methods
Na minha lista:
| Publicado no: | Algorithms vol. 18, no. 7 (2025), p. 448-471 |
|---|---|
| Autor principal: | |
| Outros Autores: | |
| Publicado em: |
MDPI AG
|
| Assuntos: | |
| Acesso em linha: | Citation/Abstract Full Text + Graphics Full Text - PDF |
| Tags: |
Sem tags, seja o primeiro a adicionar uma tag!
|
MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3233031667 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 1999-4893 | ||
| 024 | 7 | |a 10.3390/a18070448 |2 doi | |
| 035 | |a 3233031667 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231333 |2 nlm | ||
| 100 | 1 | |a Liu Ruofei |u Center for Balance Architecture, Zhejiang University, Hangzhou 310028, China; rf_liu2025@163.com | |
| 245 | 1 | |a High-Precision Chip Detection Using YOLO-Based Methods | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Machining chips are directly related to both the machining quality and tool condition. However, detecting chips from images in industrial settings poses challenges in terms of model accuracy and computational speed. We firstly present a novel framework called GM-YOLOv11-DNMS to track the chips, followed by a video-level post-processing algorithm for chip counting in videos. GM-YOLOv11-DNMS has two main improvements: (1) it replaces the CNN layers with a ghost module in YOLOv11n, significantly reducing the computational cost while maintaining the detection performance, and (2) it uses a new dynamic non-maximum suppression (DNMS) method, which dynamically adjusts the thresholds to improve the detection accuracy. The post-processing method uses a trigger signal from rising edges to improve chip counting in video streams. Experimental results show that the ghost module reduces the FLOPs from 6.48 G to 5.72 G compared to YOLOv11n, with a negligible accuracy loss, while the DNMS algorithm improves the debris detection precision across different YOLO versions. The proposed framework achieves precision, recall, and mAP@0.5 values of 97.04%, 96.38%, and 95.56%, respectively, in image-based detection tasks. In video-based experiments, the proposed video-level post-processing algorithm combined with GM-YOLOv11-DNMS achieves crack–debris counting accuracy of 90.14%. This lightweight and efficient approach is particularly effective in detecting small-scale objects within images and accurately analyzing dynamic debris in video sequences, providing a robust solution for automated debris monitoring in machine tool processing applications. | |
| 653 | |a Accuracy | ||
| 653 | |a Video post-production | ||
| 653 | |a Deep learning | ||
| 653 | |a Agricultural production | ||
| 653 | |a Ghosts | ||
| 653 | |a Machining | ||
| 653 | |a Neural networks | ||
| 653 | |a Machine tools | ||
| 653 | |a Computing costs | ||
| 653 | |a Algorithms | ||
| 653 | |a Video data | ||
| 653 | |a Images | ||
| 653 | |a Modules | ||
| 653 | |a Debris | ||
| 653 | |a Automation | ||
| 653 | |a Object recognition | ||
| 653 | |a Morphology | ||
| 700 | 1 | |a Zhu Junjiang |u College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China | |
| 773 | 0 | |t Algorithms |g vol. 18, no. 7 (2025), p. 448-471 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3233031667/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3233031667/fulltextwithgraphics/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3233031667/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |